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  • Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

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    Accepted Manuscript (AM)
    Author(s)
    Mayfield, Helen J
    Bertone, Edoardo
    Smith, Carl
    Sahin, Oz
    Griffith University Author(s)
    Sahin, Oz
    Bertone, Edoardo
    Mayfield, Helen
    Year published
    2020
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    Abstract
    Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact ...
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    Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field.
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    Journal Title
    Mathematics and Computers in Simulation
    DOI
    https://doi.org/10.1016/j.matcom.2019.07.005
    Copyright Statement
    © 2019 IMACS/Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Mathematical sciences
    Physical sciences
    Publication URI
    http://hdl.handle.net/10072/386685
    Collection
    • Journal articles

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